CAPRI: Efficient Inference of Cancer Progression Models from Cross-sectional Data

TL;DR

CAPRI is a novel algorithm that efficiently infers cancer progression models from cross-sectional genomic data, outperforming existing methods in accuracy, robustness to noise, and convergence, with applications to leukemia research.

Contribution

The paper introduces CAPRI, a new inference algorithm combining probabilistic scoring, bootstrap, and maximum likelihood, improving accuracy and robustness in reconstructing cancer progression models from cross-sectional data.

Findings

CAPRI outperforms state-of-the-art algorithms in accuracy and convergence.

CAPRI is robust to data noise and limited sample sizes.

Application to leukemia data revealed novel genomic relations.

Abstract

We devise a novel inference algorithm to effectively solve the cancer progression model reconstruction problem. Our empirical analysis of the accuracy and convergence rate of our algorithm, CAncer PRogression Inference (CAPRI), shows that it outperforms the state-of-the-art algorithms addressing similar problems. Motivation: Several cancer-related genomic data have become available (e.g., The Cancer Genome Atlas, TCGA) typically involving hundreds of patients. At present, most of these data are aggregated in a cross-sectional fashion providing all measurements at the time of diagnosis.Our goal is to infer cancer progression models from such data. These models are represented as directed acyclic graphs (DAGs) of collections of selectivity relations, where a mutation in a gene A selects for a later mutation in a gene B. Gaining insight into the structure of such progressions has the potential to improve both the stratification of patients and personalized therapy choices. Results: The CAPRI algorithm relies on a scoring method based on a probabilistic theory developed by Suppes, coupled with bootstrap and maximum likelihood inference. The resulting algorithm is efficient, achieves high accuracy, and has good complexity, also, in terms of convergence properties. CAPRI performs especially well in the presence of noise in the data, and with limited sample sizes. Moreover CAPRI, in contrast to other approaches, robustly reconstructs different types of confluent trajectories despite irregularities in the data.We also report on an ongoing investigation using CAPRI to study atypical Chronic Myeloid Leukemia, in which we uncovered non trivial selectivity relations and exclusivity patterns among key genomic events.